The Optimization Design of I-Beam Based on Multi-Objective Cellular Genetic Algorithm

Yun Wang; Yi Zhang; Zheng Liu; Fang Jun Hu

doi:10.4028/www.scientific.net/AMM.433-435.651

Paper Titles

Application of Neural Network in Fault Diagnosis of Anti-Lock Braking System
p.630

An Improved Log-MAP Algorithm For BICM-ID Embedded Turbo Codes
p.634

Solving the Job-Shop Scheduling Problem Based on Cellular Genetic Algorithm
p.639

Intelligent Control and Application of Heat Exchange Station Based on Heating Quality
p.645

The Optimization Design of I-Beam Based on Multi-Objective Cellular Genetic Algorithm
p.651

Path Optimization of Container Multimodal Transportation Based on Improved Genetic Algorithm
p.657

A Study on Particle Trajectory of Particle Swarm Optimization
p.662

Detection of Indoor Robot’s Passable Areas Based on a Single-Line Structured Light
p.667

Research on the Active Value-Added Services System Based on Grid Collaboration
p.676

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 433-435The Optimization Design of I-Beam Based on...

The Optimization Design of I-Beam Based on Multi-Objective Cellular Genetic Algorithm

Abstract:

Multi-objective cellular genetic algorithm is obviously superior to the traditional multi-objective evolutionary algorithms in terms of testing performance of algorithm. However, the algorithm still needs to be used in practical engineering problems. In consideration of the above, this paper tries to apply the multi-objective cellular genetic algorithm to solve the problem of design of I-beam. Finally, the results show that multi-objective cellular genetic algorithm has more advantages than the traditional multi-objective evolutionary algorithms in solving this kind of multi-objective problems, no matter in uniformity or expansibility of best solutions.

You might also be interested in these eBooks

Advances in Mechatronics and Control Engineering II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 433-435)

Pages:

651-656

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.433-435.651

Citation:

Cite this paper

Online since:

October 2013

Authors:

Yun Wang, Yi Zhang, Zheng Liu, Fang Jun Hu

Keywords:

Cellular Genetic Algorithm, I-Beam, Multi-Objective, Optimization Design

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Xiao Xiaowei, Xiao Di, Lin Jinguo, Xiao Yufeng. Research overview of the multi-objective optimization problem. Computer application research. (2011).

Google Scholar

[2] K. Deb, A. Pratab. A fast and elitist multi-objective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

DOI: 10.1109/4235.996017

Google Scholar

[3] Nebro antonio J, Durillo Juan J., Luna Francisco, et al. MOCell: A Cellular Genetic Algorithm for Multi-objective Optimization [J]. International journal of intelligent systems, 2009, 24: 726–746.

DOI: 10.1002/int.20358

Google Scholar

[4] Cui Xunxue. Multi-objective evolutionary algorithm and its application [M]. National defense industry press. 2008. 08.

Google Scholar

[5] Coello Cac, Christiansenad Moses. A multi-objective optimization tool for engineering design [J]. J Eng Optim, 1999, 31(3): 337–368.

Google Scholar